and \(s_n\)  is the n-th snippet. Then, each snippet is fed to a pre-trained visual encoder such as two-stream [1]}, C3D [2]}, or I3D [3]} for feature extraction. In two-stream network [1]}, snippet \(s_n\)  which is centered at \(t_n\) -th frame of the video, has an RGB frame \(x_{t_n}\) , and a stacked optical flow \(o_{t_n}\)  derived around the center frame. The RGB frame \(x_{t_n}\)  is fed to spatial network ResNet [5]}, extracting feature vector \(f_{S, n}\) . The optical flow \(o_{t_n}\)  is fed to temporal network BN-Inception [6]}, extracting feature \(f_{T, n}\) . The spatial and temporal features, \(f_{S,n}\)  and \(f_{T,n}\) , are concatenated to represent the visual feature \(f_n\)  for snippet \(s_n\) .
